Computational analysis and predictive modeling of polymorph descriptors

Abstract

BackgroundA computation approach based on integrating high throughput binding affinity comparison and binding descriptor classifications was utilized to establish the correlation among substrate properties and their affinity to Breast Cancer Resistant Protein (BCRP). The uptake rates of Mitoxantrone in the presence of various substrates were evaluated as an in vitro screening index for comparison of their binding affinity to BCRP.The effects of chemical properties of various chemotherapeutics, such as antiviral, antibiotic, calcium channel blockers, anticancer and antifungal agents, on their affinity to BCRP, were evaluated using HEK (human embryonic kidney) cells in which 3 polymorphs, namely 482R (wild type) and two mutants (482G and 482T) of BCRP, have been identified. The quantitative structure activity relationship (QSAR) model was developed using the sequential approaches of Austin Model 1 (AM1), CODESSA program, heuristic method (HM) and multiple linear regression (MLR) to establish the relationship between structural specificity of BCRP substrates and their uptake rates by BCRP polymorphs.ResultsThe BCRP mutations may induce conformational changes as manifested by the altered uptake rates of Mitoxantrone by BCRP in the presence of other competitive binding substrates that have a varying degree of affinities toward BCRP efflux. This study also revealed that the binding affinity of test substrates to each polymorph was affected by varying descriptors, such as constitutional, topological, geometrical, electrostatic, thermodynamic, and quantum chemical descriptors.ConclusionDescriptors involved with the net surface charge and energy level of substrates seem to be the common integral factors for defining binding specificity of selected substrates to BCRP polymorph. The reproducible outcomes and validation process further supported the accuracy of the computational model in assessing the correlation among descriptors involved with substrate affinity to BCRP polymorph. A quantitative computation approach will provide important structural insight into optimal designing of new chemotherapeutic agents with improved pharmacological efficacies.

Structure–Biological Function Relationship Extended to Mitotic Arrest-Deficient 2-Like Protein Mad2 Native and Mutants-New Opportunity for Genetic Disorder Control

“…Also, contribution of PCA = 1–3 was very weak and may be irrelevant in all three QSAR models ( q 2 less than 0.30, r 2 less than 0.70). Also Fisher test, RMSE (root mean square error) and cross-validated-RMSE were calculated [ 36 ].…”

Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage

“…The predictive in silico models for BCRP substrates are somewhat limited. Briefly, Hazai et al 33 used support vector machines (SVM) to build a model to predict wild-type BCRP substrates; Zhong et al 34 employed genetic algorithmconjugate gradient-SVM (GA-CG-SVM) to discriminate substrates and nonsubstrates of BCRP; Sedykh et al 35 developed a set of QSAR models using SVM, random forests (RF), and k-nearest neighbors for identification of both substrates and inhibitors of 11 intestinal transporters, including BCRP; Erićet al 36 reported artificial neural network-(ANN) and SVM-based model ensembles for the prediction of transport and inhibition of Pgp and BCRP; Garg et al 37 reported an in silico SVM model for the classification of BCRP substrates and nonsubstrates, which can be used in tandem with a second one aimed to estimate the BBB permeability; Lee et al 38 developed a linear QSAR model to establish the relationship between specificity of BCRP substrates and their uptake rates by BCRP polymorphs. Finally, Ose et al 39 developed an SVM-based prediction system to predict substrates of 7 categories of drug transporters (among them, BCRP).…”

A cascade computer model for mocrobicide diffusivity from mucoadhesive formulations

“…Advances in computer technology associated with the machine learning process have brought up the vital improvements in strategies for prevention and treatment of various diseases [ 4 , 5 ]. The computer based analysis techniques including artificial neural networks (ANN) and K-Nearest Neighbors Model (KNN) model made it possible to assess and predict the pharmaceutical parameters through the data mining methods [ 6 – 8 ].…”